Overview

Dataset statistics

Number of variables18
Number of observations234522
Missing cells2306313
Missing cells (%)54.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.2 MiB
Average record size in memory144.0 B

Variable types

Categorical3
Numeric15

Warnings

dep has a high cardinality: 101 distinct values High cardinality
date_de_passage has a high cardinality: 387 distinct values High cardinality
nbre_pass_corona is highly correlated with nbre_pass_corona_h and 1 other fieldsHigh correlation
nbre_pass_tot is highly correlated with nbre_pass_tot_h and 1 other fieldsHigh correlation
nbre_hospit_corona is highly correlated with nbre_hospit_corona_h and 1 other fieldsHigh correlation
nbre_pass_corona_h is highly correlated with nbre_pass_corona and 2 other fieldsHigh correlation
nbre_pass_corona_f is highly correlated with nbre_pass_corona and 1 other fieldsHigh correlation
nbre_pass_tot_h is highly correlated with nbre_pass_tot and 1 other fieldsHigh correlation
nbre_pass_tot_f is highly correlated with nbre_pass_tot and 1 other fieldsHigh correlation
nbre_hospit_corona_h is highly correlated with nbre_hospit_corona and 1 other fieldsHigh correlation
nbre_hospit_corona_f is highly correlated with nbre_hospit_coronaHigh correlation
nbre_acte_corona is highly correlated with nbre_acte_corona_h and 1 other fieldsHigh correlation
nbre_acte_tot is highly correlated with nbre_acte_tot_h and 1 other fieldsHigh correlation
nbre_acte_corona_h is highly correlated with nbre_acte_corona and 1 other fieldsHigh correlation
nbre_acte_corona_f is highly correlated with nbre_acte_corona and 1 other fieldsHigh correlation
nbre_acte_tot_h is highly correlated with nbre_acte_tot and 1 other fieldsHigh correlation
nbre_acte_tot_f is highly correlated with nbre_acte_tot and 1 other fieldsHigh correlation
nbre_pass_corona has 2677 (1.1%) missing values Missing
nbre_pass_tot has 2677 (1.1%) missing values Missing
nbre_hospit_corona has 2677 (1.1%) missing values Missing
nbre_pass_corona_h has 195828 (83.5%) missing values Missing
nbre_pass_corona_f has 195828 (83.5%) missing values Missing
nbre_pass_tot_h has 195828 (83.5%) missing values Missing
nbre_pass_tot_f has 195828 (83.5%) missing values Missing
nbre_hospit_corona_h has 195828 (83.5%) missing values Missing
nbre_hospit_corona_f has 195828 (83.5%) missing values Missing
nbre_acte_corona has 128207 (54.7%) missing values Missing
nbre_acte_tot has 128207 (54.7%) missing values Missing
nbre_acte_corona_h has 216725 (92.4%) missing values Missing
nbre_acte_corona_f has 216725 (92.4%) missing values Missing
nbre_acte_tot_h has 216725 (92.4%) missing values Missing
nbre_acte_tot_f has 216725 (92.4%) missing values Missing
dep is uniformly distributed Uniform
date_de_passage is uniformly distributed Uniform
sursaud_cl_age_corona is uniformly distributed Uniform
nbre_pass_corona has 108098 (46.1%) zeros Zeros
nbre_hospit_corona has 142471 (60.7%) zeros Zeros
nbre_pass_corona_h has 10994 (4.7%) zeros Zeros
nbre_pass_corona_f has 11186 (4.8%) zeros Zeros
nbre_hospit_corona_h has 16197 (6.9%) zeros Zeros
nbre_hospit_corona_f has 17616 (7.5%) zeros Zeros
nbre_acte_corona has 44346 (18.9%) zeros Zeros
nbre_acte_corona_h has 3556 (1.5%) zeros Zeros
nbre_acte_corona_f has 2976 (1.3%) zeros Zeros

Reproduction

Analysis started2021-03-18 15:48:44.892620
Analysis finished2021-03-18 15:49:13.267764
Duration28.38 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

dep
Categorical

HIGH CARDINALITY
UNIFORM

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
64
 
2322
30
 
2322
972
 
2322
78
 
2322
23
 
2322
Other values (96)
222912 

Length

Max length3
Median length2
Mean length2.04950495
Min length2

Characters and Unicode

Total characters480654
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
642322
 
1.0%
302322
 
1.0%
9722322
 
1.0%
782322
 
1.0%
232322
 
1.0%
832322
 
1.0%
092322
 
1.0%
052322
 
1.0%
792322
 
1.0%
662322
 
1.0%
Other values (91)211302
90.1%
2021-03-18T16:49:13.469970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
642322
 
1.0%
392322
 
1.0%
782322
 
1.0%
232322
 
1.0%
832322
 
1.0%
092322
 
1.0%
052322
 
1.0%
792322
 
1.0%
662322
 
1.0%
462322
 
1.0%
Other values (91)211302
90.1%

Most occurring characters

ValueCountFrequency (%)
755728
11.6%
251084
10.6%
148762
10.1%
348762
10.1%
448762
10.1%
546440
9.7%
646440
9.7%
946440
9.7%
844118
9.2%
039474
8.2%
Other values (2)4644
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number476010
99.0%
Uppercase Letter4644
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
755728
11.7%
251084
10.7%
148762
10.2%
348762
10.2%
448762
10.2%
546440
9.8%
646440
9.8%
946440
9.8%
844118
9.3%
039474
8.3%
ValueCountFrequency (%)
A2322
50.0%
B2322
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common476010
99.0%
Latin4644
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
755728
11.7%
251084
10.7%
148762
10.2%
348762
10.2%
448762
10.2%
546440
9.8%
646440
9.8%
946440
9.8%
844118
9.3%
039474
8.3%
ValueCountFrequency (%)
A2322
50.0%
B2322
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII480654
100.0%

Most frequent character per block

ValueCountFrequency (%)
755728
11.6%
251084
10.6%
148762
10.1%
348762
10.1%
448762
10.1%
546440
9.7%
646440
9.7%
946440
9.7%
844118
9.2%
039474
8.2%
Other values (2)4644
 
1.0%

date_de_passage
Categorical

HIGH CARDINALITY
UNIFORM

Distinct387
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2020-08-28
 
606
2020-09-08
 
606
2020-07-05
 
606
2020-04-17
 
606
2020-03-22
 
606
Other values (382)
231492 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2345220
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-02-24
2nd row2020-02-24
3rd row2020-02-24
4th row2020-02-24
5th row2020-02-24
ValueCountFrequency (%)
2020-08-28606
 
0.3%
2020-09-08606
 
0.3%
2020-07-05606
 
0.3%
2020-04-17606
 
0.3%
2020-03-22606
 
0.3%
2021-02-26606
 
0.3%
2020-03-16606
 
0.3%
2020-05-09606
 
0.3%
2020-10-06606
 
0.3%
2020-10-05606
 
0.3%
Other values (377)228462
97.4%
2021-03-18T16:49:13.657908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-08-28606
 
0.3%
2020-09-08606
 
0.3%
2020-07-05606
 
0.3%
2020-04-17606
 
0.3%
2020-03-22606
 
0.3%
2021-02-26606
 
0.3%
2020-03-16606
 
0.3%
2020-05-09606
 
0.3%
2020-10-06606
 
0.3%
2020-10-05606
 
0.3%
Other values (377)228462
97.4%

Most occurring characters

ValueCountFrequency (%)
0713868
30.4%
2607212
25.9%
-469044
20.0%
1242400
 
10.3%
362418
 
2.7%
542420
 
1.8%
441814
 
1.8%
641814
 
1.8%
741814
 
1.8%
841814
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1876176
80.0%
Dash Punctuation469044
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0713868
38.0%
2607212
32.4%
1242400
 
12.9%
362418
 
3.3%
542420
 
2.3%
441814
 
2.2%
641814
 
2.2%
741814
 
2.2%
841814
 
2.2%
940602
 
2.2%
ValueCountFrequency (%)
-469044
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2345220
100.0%

Most frequent character per script

ValueCountFrequency (%)
0713868
30.4%
2607212
25.9%
-469044
20.0%
1242400
 
10.3%
362418
 
2.7%
542420
 
1.8%
441814
 
1.8%
641814
 
1.8%
741814
 
1.8%
841814
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2345220
100.0%

Most frequent character per block

ValueCountFrequency (%)
0713868
30.4%
2607212
25.9%
-469044
20.0%
1242400
 
10.3%
362418
 
2.7%
542420
 
1.8%
441814
 
1.8%
641814
 
1.8%
741814
 
1.8%
841814
 
1.8%

sursaud_cl_age_corona
Categorical

UNIFORM

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
E
39087 
A
39087 
C
39087 
0
39087 
D
39087 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234522
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd rowA
3rd rowB
4th rowC
5th rowD
ValueCountFrequency (%)
E39087
16.7%
A39087
16.7%
C39087
16.7%
039087
16.7%
D39087
16.7%
B39087
16.7%
2021-03-18T16:49:13.807086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:49:13.857993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b39087
16.7%
d39087
16.7%
039087
16.7%
e39087
16.7%
a39087
16.7%
c39087
16.7%

Most occurring characters

ValueCountFrequency (%)
039087
16.7%
A39087
16.7%
B39087
16.7%
C39087
16.7%
D39087
16.7%
E39087
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter195435
83.3%
Decimal Number39087
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
A39087
20.0%
B39087
20.0%
C39087
20.0%
D39087
20.0%
E39087
20.0%
ValueCountFrequency (%)
039087
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin195435
83.3%
Common39087
 
16.7%

Most frequent character per script

ValueCountFrequency (%)
A39087
20.0%
B39087
20.0%
C39087
20.0%
D39087
20.0%
E39087
20.0%
ValueCountFrequency (%)
039087
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII234522
100.0%

Most frequent character per block

ValueCountFrequency (%)
039087
16.7%
A39087
16.7%
B39087
16.7%
C39087
16.7%
D39087
16.7%
E39087
16.7%

nbre_pass_corona
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct251
Distinct (%)0.1%
Missing2677
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean3.432090405
Minimum0
Maximum635
Zeros108098
Zeros (%)46.1%
Memory size1.8 MiB
2021-03-18T16:49:13.942985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile15
Maximum635
Range635
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.37553732
Coefficient of variation (CV)3.023095576
Kurtosis437.680436
Mean3.432090405
Median Absolute Deviation (MAD)1
Skewness14.58621493
Sum795713
Variance107.6517747
MonotocityNot monotonic
2021-03-18T16:49:14.039059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0108098
46.1%
137180
 
15.9%
220957
 
8.9%
313504
 
5.8%
49373
 
4.0%
57016
 
3.0%
65164
 
2.2%
74239
 
1.8%
83449
 
1.5%
92720
 
1.2%
Other values (241)20145
 
8.6%
(Missing)2677
 
1.1%
ValueCountFrequency (%)
0108098
46.1%
137180
 
15.9%
220957
 
8.9%
313504
 
5.8%
49373
 
4.0%
ValueCountFrequency (%)
6351
< 0.1%
5491
< 0.1%
5371
< 0.1%
5361
< 0.1%
5251
< 0.1%

nbre_pass_tot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1633
Distinct (%)0.7%
Missing2677
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean117.8979577
Minimum0
Maximum2240
Zeros3
Zeros (%)< 0.1%
Memory size1.8 MiB
2021-03-18T16:49:14.143284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q129
median59
Q3128
95-th percentile430
Maximum2240
Range2240
Interquartile range (IQR)99

Descriptive statistics

Standard deviation172.8076079
Coefficient of variation (CV)1.465738773
Kurtosis20.40682253
Mean117.8979577
Median Absolute Deviation (MAD)37
Skewness3.84231659
Sum27334052
Variance29862.46934
MonotocityNot monotonic
2021-03-18T16:49:14.238598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172617
 
1.1%
192613
 
1.1%
182612
 
1.1%
202604
 
1.1%
242567
 
1.1%
232521
 
1.1%
222491
 
1.1%
282483
 
1.1%
262466
 
1.1%
252465
 
1.1%
Other values (1623)206406
88.0%
(Missing)2677
 
1.1%
ValueCountFrequency (%)
03
 
< 0.1%
1583
0.2%
2758
0.3%
3981
0.4%
41034
0.4%
ValueCountFrequency (%)
22401
< 0.1%
22271
< 0.1%
21881
< 0.1%
21021
< 0.1%
20901
< 0.1%

nbre_hospit_corona
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct118
Distinct (%)0.1%
Missing2677
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1.569423537
Minimum0
Maximum203
Zeros142471
Zeros (%)60.7%
Memory size1.8 MiB
2021-03-18T16:49:14.338773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7
Maximum203
Range203
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.452421075
Coefficient of variation (CV)2.836978654
Kurtosis167.7011925
Mean1.569423537
Median Absolute Deviation (MAD)0
Skewness9.229332032
Sum363863
Variance19.82405343
MonotocityNot monotonic
2021-03-18T16:49:14.445520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0142471
60.7%
134834
 
14.9%
216710
 
7.1%
39763
 
4.2%
46598
 
2.8%
54366
 
1.9%
63193
 
1.4%
72402
 
1.0%
81856
 
0.8%
91462
 
0.6%
Other values (108)8190
 
3.5%
(Missing)2677
 
1.1%
ValueCountFrequency (%)
0142471
60.7%
134834
 
14.9%
216710
 
7.1%
39763
 
4.2%
46598
 
2.8%
ValueCountFrequency (%)
2031
< 0.1%
1931
< 0.1%
1681
< 0.1%
1571
< 0.1%
1501
< 0.1%

nbre_pass_corona_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct144
Distinct (%)0.4%
Missing195828
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean5.019408694
Minimum0
Maximum320
Zeros10994
Zeros (%)4.7%
Memory size1.8 MiB
2021-03-18T16:49:14.556979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile19
Maximum320
Range320
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.33690659
Coefficient of variation (CV)2.059387315
Kurtosis131.3806204
Mean5.019408694
Median Absolute Deviation (MAD)2
Skewness8.401733644
Sum194221
Variance106.8516379
MonotocityNot monotonic
2021-03-18T16:49:14.946460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010994
 
4.7%
16071
 
2.6%
24292
 
1.8%
33187
 
1.4%
42413
 
1.0%
51845
 
0.8%
61474
 
0.6%
71159
 
0.5%
8889
 
0.4%
9835
 
0.4%
Other values (134)5535
 
2.4%
(Missing)195828
83.5%
ValueCountFrequency (%)
010994
4.7%
16071
2.6%
24292
 
1.8%
33187
 
1.4%
42413
 
1.0%
ValueCountFrequency (%)
3201
< 0.1%
2801
< 0.1%
2541
< 0.1%
2441
< 0.1%
2402
< 0.1%

nbre_pass_corona_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct152
Distinct (%)0.4%
Missing195828
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean5.261513413
Minimum0
Maximum315
Zeros11186
Zeros (%)4.8%
Memory size1.8 MiB
2021-03-18T16:49:15.052466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile21
Maximum315
Range315
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.94614662
Coefficient of variation (CV)2.080417888
Kurtosis135.0207536
Mean5.261513413
Median Absolute Deviation (MAD)2
Skewness8.471297016
Sum203589
Variance119.8181259
MonotocityNot monotonic
2021-03-18T16:49:15.149550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011186
 
4.8%
16020
 
2.6%
24103
 
1.7%
33047
 
1.3%
42240
 
1.0%
51879
 
0.8%
61455
 
0.6%
71143
 
0.5%
8936
 
0.4%
9785
 
0.3%
Other values (142)5900
 
2.5%
(Missing)195828
83.5%
ValueCountFrequency (%)
011186
4.8%
16020
2.6%
24103
 
1.7%
33047
 
1.3%
42240
 
1.0%
ValueCountFrequency (%)
3151
< 0.1%
2921
< 0.1%
2851
< 0.1%
2831
< 0.1%
2691
< 0.1%

nbre_pass_tot_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct933
Distinct (%)2.4%
Missing195828
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean183.3007185
Minimum0
Maximum1176
Zeros2
Zeros (%)< 0.1%
Memory size1.8 MiB
2021-03-18T16:49:15.249455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q178
median135
Q3236
95-th percentile487
Maximum1176
Range1176
Interquartile range (IQR)158

Descriptive statistics

Standard deviation152.1908554
Coefficient of variation (CV)0.8302796447
Kurtosis4.090970279
Mean183.3007185
Median Absolute Deviation (MAD)69
Skewness1.819323611
Sum7092638
Variance23162.05647
MonotocityNot monotonic
2021-03-18T16:49:15.349517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66230
 
0.1%
68220
 
0.1%
69217
 
0.1%
77217
 
0.1%
82213
 
0.1%
71213
 
0.1%
83210
 
0.1%
55210
 
0.1%
95209
 
0.1%
86209
 
0.1%
Other values (923)36546
 
15.6%
(Missing)195828
83.5%
ValueCountFrequency (%)
02
 
< 0.1%
115
< 0.1%
218
< 0.1%
313
< 0.1%
417
< 0.1%
ValueCountFrequency (%)
11761
< 0.1%
11561
< 0.1%
11391
< 0.1%
11191
< 0.1%
10781
< 0.1%

nbre_pass_tot_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct877
Distinct (%)2.3%
Missing195828
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean169.8685326
Minimum0
Maximum1084
Zeros10
Zeros (%)< 0.1%
Memory size1.8 MiB
2021-03-18T16:49:15.452617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q174
median125
Q3218
95-th percentile451
Maximum1084
Range1084
Interquartile range (IQR)144

Descriptive statistics

Standard deviation139.8096688
Coefficient of variation (CV)0.8230463094
Kurtosis4.065420145
Mean169.8685326
Median Absolute Deviation (MAD)63
Skewness1.790530941
Sum6572893
Variance19546.7435
MonotocityNot monotonic
2021-03-18T16:49:15.553757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73264
 
0.1%
96240
 
0.1%
76235
 
0.1%
48233
 
0.1%
77233
 
0.1%
70228
 
0.1%
87228
 
0.1%
78226
 
0.1%
83226
 
0.1%
89226
 
0.1%
Other values (867)36355
 
15.5%
(Missing)195828
83.5%
ValueCountFrequency (%)
010
< 0.1%
120
< 0.1%
219
< 0.1%
315
< 0.1%
420
< 0.1%
ValueCountFrequency (%)
10841
< 0.1%
10511
< 0.1%
10491
< 0.1%
10461
< 0.1%
10451
< 0.1%

nbre_hospit_corona_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct83
Distinct (%)0.2%
Missing195828
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean2.588023983
Minimum0
Maximum130
Zeros16197
Zeros (%)6.9%
Memory size1.8 MiB
2021-03-18T16:49:15.651915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum130
Range130
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.144925455
Coefficient of variation (CV)1.987974412
Kurtosis69.00555221
Mean2.588023983
Median Absolute Deviation (MAD)1
Skewness6.026690935
Sum100141
Variance26.47025794
MonotocityNot monotonic
2021-03-18T16:49:15.757089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016197
 
6.9%
17212
 
3.1%
24235
 
1.8%
32736
 
1.2%
41770
 
0.8%
51340
 
0.6%
6991
 
0.4%
7753
 
0.3%
8611
 
0.3%
9461
 
0.2%
Other values (73)2388
 
1.0%
(Missing)195828
83.5%
ValueCountFrequency (%)
016197
6.9%
17212
3.1%
24235
 
1.8%
32736
 
1.2%
41770
 
0.8%
ValueCountFrequency (%)
1301
< 0.1%
1271
< 0.1%
1111
< 0.1%
1021
< 0.1%
961
< 0.1%

nbre_hospit_corona_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct60
Distinct (%)0.2%
Missing195828
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean2.113480126
Minimum0
Maximum76
Zeros17616
Zeros (%)7.5%
Memory size1.8 MiB
2021-03-18T16:49:15.860946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum76
Range76
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.993451818
Coefficient of variation (CV)1.889514725
Kurtosis35.67383143
Mean2.113480126
Median Absolute Deviation (MAD)1
Skewness4.632348907
Sum81779
Variance15.94765743
MonotocityNot monotonic
2021-03-18T16:49:15.960547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017616
 
7.5%
17177
 
3.1%
24107
 
1.8%
32690
 
1.1%
41756
 
0.7%
51260
 
0.5%
6878
 
0.4%
7615
 
0.3%
8476
 
0.2%
9378
 
0.2%
Other values (50)1741
 
0.7%
(Missing)195828
83.5%
ValueCountFrequency (%)
017616
7.5%
17177
3.1%
24107
 
1.8%
32690
 
1.1%
41756
 
0.7%
ValueCountFrequency (%)
761
< 0.1%
671
< 0.1%
651
< 0.1%
631
< 0.1%
591
< 0.1%

nbre_acte_corona
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct156
Distinct (%)0.1%
Missing128207
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean3.593782627
Minimum0
Maximum259
Zeros44346
Zeros (%)18.9%
Memory size1.8 MiB
2021-03-18T16:49:16.063666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16
Maximum259
Range259
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.319660524
Coefficient of variation (CV)2.315014954
Kurtosis109.4055752
Mean3.593782627
Median Absolute Deviation (MAD)1
Skewness7.587798301
Sum382073
Variance69.21675123
MonotocityNot monotonic
2021-03-18T16:49:16.165640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044346
 
18.9%
118203
 
7.8%
210141
 
4.3%
36518
 
2.8%
44679
 
2.0%
53505
 
1.5%
62782
 
1.2%
72191
 
0.9%
81862
 
0.8%
91462
 
0.6%
Other values (146)10626
 
4.5%
(Missing)128207
54.7%
ValueCountFrequency (%)
044346
18.9%
118203
7.8%
210141
 
4.3%
36518
 
2.8%
44679
 
2.0%
ValueCountFrequency (%)
2591
< 0.1%
2521
< 0.1%
2511
< 0.1%
2401
< 0.1%
2231
< 0.1%

nbre_acte_tot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct922
Distinct (%)0.9%
Missing128207
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean62.08117387
Minimum1
Maximum1444
Zeros0
Zeros (%)0.0%
Memory size1.8 MiB
2021-03-18T16:49:16.268353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median28
Q372
95-th percentile227
Maximum1444
Range1443
Interquartile range (IQR)60

Descriptive statistics

Standard deviation96.70692136
Coefficient of variation (CV)1.557749561
Kurtosis25.05883401
Mean62.08117387
Median Absolute Deviation (MAD)20
Skewness4.191652358
Sum6600160
Variance9352.228638
MonotocityNot monotonic
2021-03-18T16:49:16.367625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92773
 
1.2%
72754
 
1.2%
82683
 
1.1%
62670
 
1.1%
102620
 
1.1%
52550
 
1.1%
112411
 
1.0%
122406
 
1.0%
42315
 
1.0%
132313
 
1.0%
Other values (912)80820
34.5%
(Missing)128207
54.7%
ValueCountFrequency (%)
1935
 
0.4%
21513
0.6%
31850
0.8%
42315
1.0%
52550
1.1%
ValueCountFrequency (%)
14441
< 0.1%
14141
< 0.1%
13791
< 0.1%
13091
< 0.1%
12621
< 0.1%

nbre_acte_corona_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct79
Distinct (%)0.4%
Missing216725
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean4.688318256
Minimum0
Maximum121
Zeros3556
Zeros (%)1.5%
Memory size1.8 MiB
2021-03-18T16:49:16.470785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile16
Maximum121
Range121
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.875911437
Coefficient of variation (CV)1.466605094
Kurtosis40.50809719
Mean4.688318256
Median Absolute Deviation (MAD)2
Skewness4.721543848
Sum83438
Variance47.27815809
MonotocityNot monotonic
2021-03-18T16:49:16.574618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03556
 
1.5%
12755
 
1.2%
22266
 
1.0%
31851
 
0.8%
41479
 
0.6%
51122
 
0.5%
6783
 
0.3%
7710
 
0.3%
8522
 
0.2%
9407
 
0.2%
Other values (69)2346
 
1.0%
(Missing)216725
92.4%
ValueCountFrequency (%)
03556
1.5%
12755
1.2%
22266
1.0%
31851
0.8%
41479
0.6%
ValueCountFrequency (%)
1211
< 0.1%
1141
< 0.1%
1101
< 0.1%
1071
< 0.1%
1031
< 0.1%

nbre_acte_corona_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct101
Distinct (%)0.6%
Missing216725
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean6.057088273
Minimum0
Maximum144
Zeros2976
Zeros (%)1.3%
Memory size1.8 MiB
2021-03-18T16:49:16.676856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile21
Maximum144
Range144
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.844324298
Coefficient of variation (CV)1.460161038
Kurtosis38.83895723
Mean6.057088273
Median Absolute Deviation (MAD)3
Skewness4.719620043
Sum107798
Variance78.22207228
MonotocityNot monotonic
2021-03-18T16:49:16.779001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02976
 
1.3%
12203
 
0.9%
21990
 
0.8%
31836
 
0.8%
41448
 
0.6%
51186
 
0.5%
6926
 
0.4%
7775
 
0.3%
8614
 
0.3%
9507
 
0.2%
Other values (91)3336
 
1.4%
(Missing)216725
92.4%
ValueCountFrequency (%)
02976
1.3%
12203
0.9%
21990
0.8%
31836
0.8%
41448
0.6%
ValueCountFrequency (%)
1441
 
< 0.1%
1383
< 0.1%
1241
 
< 0.1%
1221
 
< 0.1%
1201
 
< 0.1%

nbre_acte_tot_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct444
Distinct (%)2.5%
Missing216725
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean78.94100129
Minimum0
Maximum621
Zeros11
Zeros (%)< 0.1%
Memory size1.8 MiB
2021-03-18T16:49:16.886441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q136
median55
Q394
95-th percentile235
Maximum621
Range621
Interquartile range (IQR)58

Descriptive statistics

Standard deviation70.99220626
Coefficient of variation (CV)0.899307142
Kurtosis6.175302752
Mean78.94100129
Median Absolute Deviation (MAD)24
Skewness2.254087568
Sum1404913
Variance5039.89335
MonotocityNot monotonic
2021-03-18T16:49:16.985932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39291
 
0.1%
43285
 
0.1%
40273
 
0.1%
36260
 
0.1%
44258
 
0.1%
45248
 
0.1%
38247
 
0.1%
37246
 
0.1%
41245
 
0.1%
48243
 
0.1%
Other values (434)15201
 
6.5%
(Missing)216725
92.4%
ValueCountFrequency (%)
011
< 0.1%
18
 
< 0.1%
224
< 0.1%
323
< 0.1%
426
< 0.1%
ValueCountFrequency (%)
6211
< 0.1%
6201
< 0.1%
5651
< 0.1%
5551
< 0.1%
5522
< 0.1%

nbre_acte_tot_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct570
Distinct (%)3.2%
Missing216725
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean106.6834298
Minimum0
Maximum824
Zeros1
Zeros (%)< 0.1%
Memory size1.8 MiB
2021-03-18T16:49:17.092007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q151
median75
Q3127
95-th percentile314
Maximum824
Range824
Interquartile range (IQR)76

Descriptive statistics

Standard deviation93.76426974
Coefficient of variation (CV)0.8789019056
Kurtosis6.233892929
Mean106.6834298
Median Absolute Deviation (MAD)32
Skewness2.27167665
Sum1898645
Variance8791.738281
MonotocityNot monotonic
2021-03-18T16:49:17.189631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58216
 
0.1%
63215
 
0.1%
61206
 
0.1%
66206
 
0.1%
59204
 
0.1%
55201
 
0.1%
68201
 
0.1%
52199
 
0.1%
49198
 
0.1%
64197
 
0.1%
Other values (560)15754
 
6.7%
(Missing)216725
92.4%
ValueCountFrequency (%)
01
 
< 0.1%
16
< 0.1%
25
< 0.1%
310
< 0.1%
46
< 0.1%
ValueCountFrequency (%)
8241
< 0.1%
8231
< 0.1%
7931
< 0.1%
7421
< 0.1%
7071
< 0.1%

Interactions

2021-03-18T16:48:51.729988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:51.843695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:51.939840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.036327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.128829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.220077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.310909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.401852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.500341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.595057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.685797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.776438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.867224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:52.956155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.061390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.166157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.254290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.339631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.425159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.510174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.593128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.678308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.773187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.862378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:53.948372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.030437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.114690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.196886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.294310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.409289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.501754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.598944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.695912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.793011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.887357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:54.983512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.087364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.187810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.285187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.380059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.476723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.573893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.665707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.760758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.865452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:55.963733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.054436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.145422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.235994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.327590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.418272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.505167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.596064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.684142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.774642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.863204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:56.950700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.041216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.127338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.222022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.313397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.404860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.494178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.586050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.677103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.763733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.854187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:57.942599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.033209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.121950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.208575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.298842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.384356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.479373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.569787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.660361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.748851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:58.974233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.081670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.168539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.258980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.346892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.436494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.524287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.609983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.697325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.780013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.872089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:48:59.963407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.052351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.139971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.227022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.314201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.398992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.486576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.572256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.659895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.746138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.831032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:00.922189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.008987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.103283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.195825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.287122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.377425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.465958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.556400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.642704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.732904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.822101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:01.912794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.002995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.090264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.184353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.273568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.371244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.465914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.560421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.654894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.747178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.843004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:02.932307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.190923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.303355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.397762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.489285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.588149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.681932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.770951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.863363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:03.959851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.056199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.152711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.238040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.328507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.425282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.514038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.600304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.688132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.774432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.865571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:04.969133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.063256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.160435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.251289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.342171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.433154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.522079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.613349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.717113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.807752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.899074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:05.991524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.082224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.168515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.258406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.343082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.436045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.527294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.627495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.728558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.836085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:06.948526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.042021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.134339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.232878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.327436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.416078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.500783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.592914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.680181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.779852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.871133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:07.964565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.055464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.144208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.235405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.537993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.650179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.743719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.836011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:08.927096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.013432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.101597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.185433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.278204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.367152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.456234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.545345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.632257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.725222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.816886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.903372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:09.994177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.082807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.173634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.264358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.357278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.437219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.525088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.607590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.690313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.773645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.853998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:10.936315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:11.021309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:11.102076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:11.189691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:11.274154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:49:11.360622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-18T16:49:17.286584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-18T16:49:17.451767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-18T16:49:17.617609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-18T16:49:17.788347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-18T16:49:11.669796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-18T16:49:12.097142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-18T16:49:12.775068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-18T16:49:13.072638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

depdate_de_passagesursaud_cl_age_coronanbre_pass_coronanbre_pass_totnbre_hospit_coronanbre_pass_corona_hnbre_pass_corona_fnbre_pass_tot_hnbre_pass_tot_fnbre_hospit_corona_hnbre_hospit_corona_fnbre_acte_coronanbre_acte_totnbre_acte_corona_hnbre_acte_corona_fnbre_acte_tot_hnbre_acte_tot_f
0012020-02-2400.0357.00.00.00.0202.0155.00.00.0NaNNaNNaNNaNNaNNaN
1012020-02-24A0.073.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2012020-02-24B0.0155.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3012020-02-24C0.061.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4012020-02-24D0.028.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5012020-02-24E0.040.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6012020-02-2500.0310.00.00.00.0177.0133.00.00.0NaNNaNNaNNaNNaNNaN
7012020-02-25A0.070.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8012020-02-25B0.0117.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9012020-02-25C0.053.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

depdate_de_passagesursaud_cl_age_coronanbre_pass_coronanbre_pass_totnbre_hospit_coronanbre_pass_corona_hnbre_pass_corona_fnbre_pass_tot_hnbre_pass_tot_fnbre_hospit_corona_hnbre_hospit_corona_fnbre_acte_coronanbre_acte_totnbre_acte_corona_hnbre_acte_corona_fnbre_acte_tot_hnbre_acte_tot_f
2345129762021-03-15B0.041.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345139762021-03-15C1.019.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345149762021-03-15D1.05.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345159762021-03-15ENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345169762021-03-1600.062.00.00.00.032.030.00.00.0NaNNaNNaNNaNNaNNaN
2345179762021-03-16A0.020.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345189762021-03-16B0.030.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345199762021-03-16C0.010.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2345209762021-03-16D0.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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